Computer-implemented models can be used to efficiently execute classifications and similarity analysis using probabilistic models generated through the principles of machine learning. Such models can be used in the field of computer security to evaluate threats (e.g. for malware and virus detection and/or avoidance). In some examples, these models can be generated in an automated manner via use of generative models in which samples further train the system and result in iteratively better models that correctly represent the best predictive capabilities expressed within a particular sample population.
In the context of computer security, discernment refers to the characterization of whether or not to allow a particular application, application module, other executable code, etc. to execute on a particular computing system or systems. In other examples, discernment may refer to identifying whether or not to allow a file to be downloaded to or opened by a particular computing system or systems, or more generally to whether or not to allow a given action by a file or program to be initiated or to be continued after being initiated.